CN110968611A - Data analysis method, device and system - Google Patents
Data analysis method, device and system Download PDFInfo
- Publication number
- CN110968611A CN110968611A CN201811144715.9A CN201811144715A CN110968611A CN 110968611 A CN110968611 A CN 110968611A CN 201811144715 A CN201811144715 A CN 201811144715A CN 110968611 A CN110968611 A CN 110968611A
- Authority
- CN
- China
- Prior art keywords
- data set
- dealer
- data
- result
- market
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000007405 data analysis Methods 0.000 title claims abstract description 37
- 230000008859 change Effects 0.000 claims abstract description 107
- 238000004458 analytical method Methods 0.000 claims abstract description 50
- 230000001174 ascending effect Effects 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 10
- 230000000694 effects Effects 0.000 claims description 8
- 238000012216 screening Methods 0.000 claims description 6
- 239000000126 substance Substances 0.000 claims description 5
- 238000004590 computer program Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- 238000004519 manufacturing process Methods 0.000 description 6
- 238000006243 chemical reaction Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 230000008901 benefit Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000009396 hybridization Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000013011 mating Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention discloses a data analysis method, a device and a system, wherein the data analysis method is applied to an enterprise server which is connected with a plurality of dealer terminals; the data analysis method comprises the following steps: determining a market situation dynamic data set of a plurality of dealer terminals in the current period; acquiring a result change data set of a plurality of dealer terminals in the current period; for each dealer terminal, performing cross combination on the market condition dynamic data set and the result change data set to obtain a relation data set; and comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals to obtain a refined analysis result. The market situation dynamic data set can represent dynamic influences of market situations of multiple dimensions where the dealer is located, the result change data set can represent the change situation of the sales performance of the multiple dimensions, the market situation dynamic data set and the result change data set are subjected to cross analysis, the dealer terminal can be comprehensively analyzed from each dimension, and a refined analysis result is obtained.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a data analysis method, apparatus, and system.
Background
Currently, a manufacturing enterprise has a plurality of sales channels, and a dealer is one of the sales channels. The manufacturing facility needs to know about the individual distributors in order to properly manage the individual distributors.
At present, a production and manufacturing enterprise generally obtains sales income amount and inventory backlog of each dealer to analyze each dealer, and the analysis dimension is small, so that the refined analysis result of each dealer cannot be obtained.
Disclosure of Invention
In view of the above, the present invention is proposed to provide a data analysis method, apparatus and system that overcome the above problems or at least partially solve the above problems, so as to finely track and analyze various dealers.
In order to solve the above problems, the present application provides the following technical features:
a data analysis method is applied to an enterprise server, and the enterprise server is connected with a plurality of dealer terminals; the data analysis method comprises the following steps:
determining a market situation dynamic data set of a plurality of dealer terminals in the current period;
acquiring a result change data set of a plurality of dealer terminals in the current period;
for each dealer terminal, performing cross combination on the market condition dynamic data set and the result change data set to obtain a relation data set;
and comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals to obtain a refined analysis result.
Optionally, the determining the market condition dynamic data set of the plurality of dealer terminals in the current period includes:
for each dealer terminal: acquiring an external market dynamic data set and an internal market dynamic data set of a dealer terminal in a current period, and forming the market dynamic data set of the current period by the external market dynamic data set and the internal market dynamic data; wherein the content of the first and second substances,
the external market dynamics data set includes: whether the current time is at least one of a judgment result of the busy season, a judgment result of whether a competitor has activities and a judgment result of policy movement;
the internal market dynamics data set includes: at least one of a judgment result of whether there is a discount, a judgment result of whether there is a promotion, and a judgment result of whether there is a new product on the market.
Optionally, the acquiring a result change data set of a plurality of dealer terminals in the current period includes:
acquiring current performance data of a plurality of dealer terminals in a current period;
calculating according to a preset rule to obtain a current result data set based on the current performance data and the historical performance data;
comparing the current result data set with the historical result data set to obtain a result change data set;
wherein the result change data set comprises result change data corresponding to a plurality of types of performance data;
if the performance data corresponding to one type in the current performance data set and the performance data corresponding to the same type in the historical performance data are within a preset range, the result change data corresponding to the type is a stable identifier;
if the performance data corresponding to one type in the current performance data set is larger than the performance data corresponding to the same type in the historical performance data, the result change data corresponding to the type is an ascending identifier;
and if the performance data corresponding to one type in the current performance data set is smaller than the performance data corresponding to the same type in the historical performance data, the result change data corresponding to the type is a descending identifier.
Optionally, the cross-combining the market condition dynamic data set and the result variation data set to obtain a relationship data set includes:
for each quotation dynamic data in the quotation dynamic data set, respectively constructing relationship data with each type of result change data in the result change data set;
a plurality of relational data make up the relational data set.
Optionally, the comprehensively analyzing the relationship data sets corresponding to the multiple dealer terminals to obtain a refined analysis result includes:
comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals, and analyzing the influence degree of the obtained different market situation dynamic data on each result change data corresponding to each dealer terminal;
and analyzing comprehensively the corresponding relation data sets of the plurality of dealer terminals to obtain one or more market situation dynamic data which cause different result change data corresponding to each dealer terminal.
Optionally, the dealer terminal is represented by an essential feature data set; wherein the essential feature data set comprises:
the method comprises the following steps of determining the type of an industry to which a dealer belongs, the location district to which the dealer belongs, the administrative level to which the dealer belongs, the judgment result of whether related products are sold in parallel, the type of an store to which the dealer belongs, the personnel service quality level, the service personnel efficiency level, the hardware equipment level of the store and an after-sale management mode;
the data analysis method comprises:
and screening and displaying the refined analysis results of the target dealer terminal from the plurality of dealer terminals according to one or more fields in the essential characteristic data set for further manual analysis.
Optionally, the method further includes: analyzing the refined analysis result of the dealer terminal according to a preset rule, and determining the adjustment type of the dealer so as to adjust the sales mode data of the dealer terminal according to the adjustment type;
wherein the adjustment types include: a support type, a restriction type, and a preference type.
A data analysis device is applied to an enterprise server, and the enterprise server is connected with a plurality of dealer terminals; the data analysis device includes:
the system comprises a determining unit, a processing unit and a processing unit, wherein the determining unit is used for determining market situation dynamic data sets of a plurality of dealer terminals in the current period;
the acquiring unit is used for acquiring a result change data set of a plurality of dealer terminals in the current period;
the combination unit is used for carrying out cross combination on the market situation dynamic data set and the result change data set to obtain a relation data set for each dealer terminal;
and the analysis unit is used for comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals to obtain a refined analysis result.
A data analysis system, comprising:
an enterprise server, and a plurality of dealer terminals connected to the enterprise server;
the enterprise server is used for determining the market situation dynamic data set of the plurality of dealer terminals in the current period;
acquiring a result change data set of a plurality of dealer terminals in the current period; for each dealer terminal, performing cross combination on the market condition dynamic data set and the result change data set to obtain a relation data set; and comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals to obtain a refined analysis result.
An apparatus comprising a processor, a memory, and a program stored on the memory and executable on the processor, the processor implementing the steps when executing the program of:
determining a market situation dynamic data set of a plurality of dealer terminals in the current period;
acquiring a result change data set of a plurality of dealer terminals in the current period;
for each dealer terminal, performing cross combination on the market condition dynamic data set and the result change data set to obtain a relation data set;
and comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals to obtain a refined analysis result.
By means of the technical scheme, the data analysis method, the data analysis device and the data analysis system can determine the quotation dynamic data sets and the result change data sets of a plurality of dealer terminals, and conduct cross analysis on the quotation dynamic data sets and the result change data sets.
The market situation dynamic data set can represent dynamic influences of market situations of multiple dimensions where the dealer is located, the result change data set can represent the change situation of the sales performance of the multiple dimensions, the market situation dynamic data set and the result change data set are subjected to cross analysis, the dealer terminal can be comprehensively analyzed from each dimension, and a refined analysis result is obtained.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 shows a schematic of the structure of a data analysis system;
FIG. 2 shows a flow chart of a method of data analysis;
fig. 3 shows a schematic configuration of the data analysis apparatus.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
To facilitate understanding, the present application provides a data analysis system. Referring to fig. 1, the data analysis system includes:
an enterprise server 100; and the combination of (a) and (b),
a plurality of dealer terminals 200 connected to the enterprise server.
The enterprise server stores an essence feature data set of a plurality of dealer terminals.
A manufacturing enterprise has a plurality of distributors, and each distributor corresponds to a distributor terminal. In order to be able to clearly identify the individual distributors, the set of intrinsic characteristic data is used to represent the distributors, i.e. to represent the distributor terminals.
The dealer terminal is represented by an essence characteristic data set including a plurality of essence characteristic data. See table 1 for an example of an intrinsic feature data set.
TABLE 1
Of course, the above-mentioned essential characteristic data sets are only schematic illustrations, and are implemented according to specific situations when implementing the present solution.
The above-described intrinsic characteristic data may be investigated and confirmed by the enterprise personnel for dealers and then stored in the enterprise server 100, so that the enterprise server stores the intrinsic characteristic bookkeeping sets of the respective dealer terminals.
The data analysis method is executed periodically, one period can be a time length of one month, one quarter, one half year, one year and the like, and the time length of a specific period can be determined according to specific situations, and is not limited herein.
The present application provides a data analysis method, see fig. 2, comprising the steps of:
step S201: the enterprise server determines the market situation dynamic data set of the plurality of dealer terminals in the current period.
It will be appreciated that for most industries, market dynamics directly impact dealer sales. Moreover, the market trends in different periods are different, and the enterprise server can determine market dynamic data sets of a plurality of dealer terminals in the current period.
For each dealer terminal: and acquiring an external quotation dynamic data set and an internal quotation dynamic data set of the dealer terminal in the current period, and forming the quotation dynamic data set of the current period by using the external quotation dynamic data set and the internal quotation dynamic data.
See table 2 for an example of a market dynamics data set.
Of course, the above-mentioned essential characteristic data sets are only schematic illustrations, and are implemented according to specific situations when implementing the present solution.
The market data set may be investigated and confirmed by the enterprise personnel for the current period of each dealer, and then stored in the enterprise server 100, so that the enterprise server stores the market data set of each dealer terminal in the current period.
The enterprise server obtains the result change data set of the plurality of dealer terminals in the current period, and includes the following steps S202 to S204.
Step S202: the enterprise server acquires current performance data of a plurality of dealer terminals in the current period.
The enterprise server 100 is connected to a plurality of dealer terminals, and the dealer terminals can transmit current performance data to the enterprise server 100 after generating the current performance data for the current period.
Step S203: and the enterprise server calculates and obtains a current result data set according to a preset rule based on the current performance data and the historical performance data.
The enterprise server obtains current performance data, and the current performance data comprises: sales amount, sales income amount, number of newly added intention customers.
The enterprise server stores historical performance data, and after acquiring the current performance data, the enterprise server can perform calculation based on the current performance data and the historical performance data to acquire a current result data set.
See table 3 for an example of a current result data set.
TABLE 3
| Current result data set |
| Percentage of sales plan completion |
| Number of newly added intention customers |
| Conversion rate of hybridization |
| Amount of sales income |
| Percentage by weight of same |
| Percent ring ratio |
The calculation process is identical for each dealer terminal, for which purpose the calculation process is based on the introduction of the individual fields in the current result data set by a dealer terminal.
For percent completion of sales plan: the current sales quantity is obtained from the current performance data of the dealer terminal, the percentage of the sales quantity to the sales plan quantity is calculated, and the percentage is determined as the sales plan completion percentage.
For the conversion to mating: and acquiring the number of newly added intention customers and the number of sales from the current performance data of the dealer terminal, calculating the percentage of the number of newly added intention customers and the number of sales, and determining the percentage as the transaction conversion rate.
For ring ratio percentages: and obtaining the sales income amount of the previous period from the historical performance data, calculating the percentage of the sales income amount of the current period to the sales income amount of the previous period, and determining the percentage as the percentage of the ring ratio.
For percent on a scale: and obtaining the sales income amount of the same period in the last year from the historical performance data, calculating the percentage of the sales income amount of the current period and the sales income amount of the same period, and determining the percentage as the percentage of the same ratio.
And forming a current result data set by the sales plan completion percentage, the number of newly added intention customers, the transaction conversion rate, the sales income amount, the percentage of the same proportion and the percentage of the ring proportion.
Step S204: and the enterprise server compares the current result data set with the historical result data set to obtain a result change data set.
The enterprise server may obtain the current result data set of the current period through step S203, and the enterprise server further stores the historical result data set of the previous period.
Comparing the current result data set with the data of the same type in the historical result data set, so as to obtain the historical result change data set. The following explains the comparison process by taking a type data as an example.
And if the performance data corresponding to one type in the current performance data set and the performance data corresponding to the same type in the historical performance data are within a preset range, the result change data corresponding to the type is a stable identifier.
And if the performance data corresponding to one type in the current performance data set is larger than the performance data corresponding to the same type in the historical performance data, the result change data corresponding to the type is an ascending identifier.
And if the performance data corresponding to one type in the current performance data set is smaller than the performance data corresponding to the same type in the historical performance data, the result change data corresponding to the type is a descending identifier.
See table 4 for an example of a resulting shift data set.
TABLE 4
Step S205: and for each dealer terminal, the enterprise server performs cross combination on the market situation dynamic data set and the result change data set to obtain a relation data set.
For each dealer terminal: the enterprise server constructs relation data for each quotation dynamic data in the quotation dynamic data set and each type of result change data in the result change data set respectively; a plurality of relational data make up the relational data set.
Taking the judgment result of whether the quotation dynamic data in the quotation dynamic data is in the peak season of sales as an example, the relevance relationship is respectively established with the sales plan completion percentage, the number of newly added intention customers, the transaction conversion rate, the sales income amount, the percentage of the same proportion and the percentage of the ring proportion. For ease of understanding, table 5 is presented.
TABLE 5
Of course, the above table only takes "the judgment result of whether the market season is good" as an example, and actually, the table should include the relationship data constructed by the market data of each market data set and the result change data of each type in the result change data set.
Step S206: and comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals to obtain a refined analysis result.
This step may include two aspects:
in a first aspect: and comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals, and analyzing the influence degree of different market situation dynamic data on each result change data corresponding to each dealer terminal.
According to the relationship data set in step S205, for each market dynamics data:
and counting a first quantity corresponding to the market dynamic data and positioned in the ascending mark in the relational data set, calculating a first ratio of the first quantity to the total quantity, and determining the ratio as the ascending influence degree of the market dynamic data.
And counting a second quantity corresponding to the dynamic market data and being in a stable mark in the relational data set, calculating a second ratio of the second quantity to the total quantity, and determining the ratio as the stable influence degree of the dynamic market data.
And counting a third quantity corresponding to the dynamic market data and being in a stable mark in the relational data set, calculating a third ratio of the third quantity to the total quantity, and determining the ratio as the descending influence degree of the dynamic market data.
For example, referring to 6 result change data corresponding to one market situation dynamic data in table 5, assuming that there are 4 ascending flags, 1 stationary flag, and one descending flag, the ascending influence degree is 2/3, the stationary influence degree is 1/6, and the descending influence degree is 1/6.
In a second aspect: and analyzing one or more market situation dynamic data which cause different result change data corresponding to each dealer terminal.
Analyzing the dealer terminal's relational data set, for each result variation data in the result variation data set:
counting the quotation dynamic data related to the result change data, determining the quantity of the quotation dynamic data, and determining one or more quotation dynamic data with the quantity determined in the sequence from high to low as the one or more quotation dynamic data causing the result change data.
For example, taking the result change data as "sales income amount" as an example, the market situation dynamic data associated with "sales income amount" in each dealer terminal is counted in the relationship data set, and the number of each market situation dynamic data is determined.
See table 6 for the number of hypothetical individual market dynamics data.
TABLE 6
Assuming that the number of the required quotation dynamic data is 3, the number of the quotation dynamic data can be sorted, and three quotation dynamic results, namely a judgment result of whether the quotation dynamic data is in a busy season, a judgment result of policy movement and a judgment result of whether the sales promotion activity exists, are found to be main reasons for increasing the sales income sum.
Of course, the above is only an example, and the step may perform the above process on each result variation data, so as to obtain the reason for the result variation data.
That is, the step can comprehensively analyze the sales condition of each dealer terminal for different dealer terminals under the condition of different market dynamic data.
Step S207: and the enterprise server outputs and displays the refined analysis result.
The technical personnel can screen and display the refined analysis result of the target dealer terminal from the plurality of dealer terminals according to one or more fields in the essential characteristic data set corresponding to the dealer terminal, so as to provide the manual further analysis.
Step S208: the enterprise server endows adjustment types to all dealers according to the analysis results of the dealers to be analyzed; wherein the adjustment types include a support type, a limit type and an offer type.
The enterprise server relates to a judgment rule suitable for the industry type, and the enterprise server can determine the adjustment type of each dealer terminal according to the preset judgment rule. The judgment rules of different industries are different, and two examples are listed below for illustration.
Assuming that the sales volume increases each time a dealer has activity, the dealer is determined to be of the offer type.
Assuming that a dealer is gradually increasing in sales, the dealer is determined to be of the support type.
Assuming that the sales volume of a dealer gradually decreases, the dealer is determined to be of the restricted type.
Step S209: and pushing the sales mode data corresponding to the adjustment type to each distributor terminal so that the distributor terminal can adjust the sales mode data.
For example, for a franchisee of the offer type, a franchise policy may be enforced on product prices, discounts, and tasks; for the support type dealer, executing a support strategy of training and service support for the dealer; for restricted type dealers, the restriction policy is enforced on product prices, discounts, and tasks, and if necessary, the dealers are revoked.
Step S210: the steps S201 to S209 are executed in a loop so as to track and analyze the sales of the respective dealer terminals.
Through the steps, the technical scheme provided by the application has the following beneficial effects:
firstly, the intrinsic characteristic data set is adopted to represent the dealer terminal, so that the influence condition of the dealer terminal by the market situation dynamic data can be analyzed according to different field combinations in the intrinsic characteristic data set.
Secondly, the market situation dynamic data and the result change data set are subjected to cross analysis, so that the influence condition of the dealer terminal on different aspects can be analyzed in a fine mode.
Thirdly, the adjustment type of the dealer terminal can be determined based on the refined analysis result so as to adjust the business model data of the dealer, so as to rationalize and manage each dealer.
Referring to fig. 3, the present application provides a data analysis apparatus applied to an enterprise server connected to a plurality of dealer terminals; the data analysis device includes:
a determining unit 31, configured to determine a market situation dynamic data set of a plurality of dealer terminals in a current period;
an acquiring unit 32, configured to acquire a result change data set of a plurality of dealer terminals in a current cycle;
a combining unit 33, configured to, for each dealer terminal, cross-combine the market situation dynamic data set and the result change data set to obtain a relationship data set;
and the analysis unit 34 is used for comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals to obtain a refined analysis result.
The determining unit 31 is configured to determine the market condition dynamic data set of a plurality of dealer terminals in the current period, and specifically includes: for each dealer terminal: acquiring an external market dynamic data set and an internal market dynamic data set of a dealer terminal in a current period, and forming the market dynamic data set of the current period by the external market dynamic data set and the internal market dynamic data; wherein the content of the first and second substances,
the external market dynamics data set includes: whether the current time is at least one of a judgment result of the busy season, a judgment result of whether a competitor has activities and a judgment result of policy movement;
the internal market dynamics data set includes: at least one of a judgment result of whether there is a discount, a judgment result of whether there is a promotion, and a judgment result of whether there is a new product on the market.
The acquiring unit 32 is configured to acquire result change data sets of a plurality of dealer terminals in a current cycle, and specifically includes: acquiring current performance data of a plurality of dealer terminals in a current period; calculating according to a preset rule to obtain a current result data set based on the current performance data and the historical performance data; comparing the current result data set with the historical result data set to obtain a result change data set; wherein the result change data set comprises result change data corresponding to a plurality of types of performance data;
if the performance data corresponding to one type in the current performance data set and the performance data corresponding to the same type in the historical performance data are within a preset range, the result change data corresponding to the type is a stable identifier;
if the performance data corresponding to one type in the current performance data set is larger than the performance data corresponding to the same type in the historical performance data, the result change data corresponding to the type is an ascending identifier;
and if the performance data corresponding to one type in the current performance data set is smaller than the performance data corresponding to the same type in the historical performance data, the result change data corresponding to the type is a descending identifier.
The combining unit 33 is configured to perform cross-combining on the market condition dynamic data set and the result change data set to obtain a relationship data set, and specifically includes: for each quotation dynamic data in the quotation dynamic data set, respectively constructing relationship data with each type of result change data in the result change data set; a plurality of relational data make up the relational data set.
The analysis unit 34 is configured to comprehensively analyze the relationship data sets corresponding to the multiple dealer terminals to obtain a refined analysis result, and specifically includes:
comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals, and analyzing the influence degree of the obtained different market situation dynamic data on each result change data corresponding to each dealer terminal; and analyzing comprehensively the corresponding relation data sets of the plurality of dealer terminals to obtain one or more market situation dynamic data which cause different result change data corresponding to each dealer terminal.
Wherein the dealer terminal is represented using an essential feature data set; wherein the essential feature data set comprises: the method comprises the following steps of determining the type of an industry to which a dealer belongs, the location district to which the dealer belongs, the administrative level to which the dealer belongs, the judgment result of whether related products are sold in parallel, the type of an store to which the dealer belongs, the personnel service quality level, the service personnel efficiency level, the hardware equipment level of the store and an after-sale management mode;
the data analysis method further comprises:
and a screening and analyzing unit 35, configured to screen and display the fine analysis result of the target dealer terminal from the plurality of dealer terminals according to one or more fields in the intrinsic characteristic data set, so as to provide for further analysis by human.
An adjustment type determining unit 36, which analyzes the refined analysis result of the dealer terminal according to a preset rule, and determines an adjustment type of the dealer terminal, so as to adjust the sales mode data of the dealer terminal according to the adjustment type; wherein the adjustment types include: a support type, a restriction type, and a preference type.
The data analysis device comprises a processor and a memory, wherein the determining unit 31, the acquiring unit 32, the combining unit 33, the analyzing unit 34, the screening analyzing unit 35, the determining and adjusting type unit 36 and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more, and each dealer can be finely tracked and analyzed by adjusting the kernel parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
An embodiment of the present invention provides a storage medium on which a program is stored, the program implementing the data analysis method when executed by a processor.
The embodiment of the invention provides a processor, which is used for running a program, wherein the data analysis method is executed when the program runs.
The embodiment of the invention provides equipment, which comprises a processor, a memory and a program which is stored on the memory and can run on the processor, wherein the processor executes the program and realizes the following steps:
determining a market situation dynamic data set of a plurality of dealer terminals in the current period;
acquiring a result change data set of a plurality of dealer terminals in the current period;
for each dealer terminal, performing cross combination on the market condition dynamic data set and the result change data set to obtain a relation data set;
and comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals to obtain a refined analysis result.
Optionally, the determining the market condition dynamic data set of the plurality of dealer terminals in the current period includes:
for each dealer terminal: acquiring an external market dynamic data set and an internal market dynamic data set of a dealer terminal in a current period, and forming the market dynamic data set of the current period by the external market dynamic data set and the internal market dynamic data; wherein the content of the first and second substances,
the external market dynamics data set includes: whether the current time is at least one of a judgment result of the busy season, a judgment result of whether a competitor has activities and a judgment result of policy movement;
the internal market dynamics data set includes: at least one of a judgment result of whether there is a discount, a judgment result of whether there is a promotion, and a judgment result of whether there is a new product on the market.
Optionally, the acquiring a result change data set of a plurality of dealer terminals in the current period includes:
acquiring current performance data of a plurality of dealer terminals in a current period;
calculating according to a preset rule to obtain a current result data set based on the current performance data and the historical performance data;
comparing the current result data set with the historical result data set to obtain a result change data set;
wherein the result change data set comprises result change data corresponding to a plurality of types of performance data;
if the performance data corresponding to one type in the current performance data set and the performance data corresponding to the same type in the historical performance data are within a preset range, the result change data corresponding to the type is a stable identifier;
if the performance data corresponding to one type in the current performance data set is larger than the performance data corresponding to the same type in the historical performance data, the result change data corresponding to the type is an ascending identifier;
and if the performance data corresponding to one type in the current performance data set is smaller than the performance data corresponding to the same type in the historical performance data, the result change data corresponding to the type is a descending identifier.
Optionally, the cross-combining the market condition dynamic data set and the result variation data set to obtain a relationship data set includes:
for each quotation dynamic data in the quotation dynamic data set, respectively constructing relationship data with each type of result change data in the result change data set;
a plurality of relational data make up the relational data set.
Optionally, the comprehensively analyzing the relationship data sets corresponding to the multiple dealer terminals to obtain a refined analysis result includes:
comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals, and analyzing the influence degree of the obtained different market situation dynamic data on each result change data corresponding to each dealer terminal;
and analyzing comprehensively the corresponding relation data sets of the plurality of dealer terminals to obtain one or more market situation dynamic data which cause different result change data corresponding to each dealer terminal.
Optionally, the dealer terminal is represented by an essential feature data set; wherein the essential feature data set comprises:
the method comprises the following steps of determining the type of an industry to which a dealer belongs, the location district to which the dealer belongs, the administrative level to which the dealer belongs, the judgment result of whether related products are sold in parallel, the type of an store to which the dealer belongs, the personnel service quality level, the service personnel efficiency level, the hardware equipment level of the store and an after-sale management mode;
the data analysis method comprises:
and screening and displaying the refined analysis results of the target dealer terminal from the plurality of dealer terminals according to one or more fields in the essential characteristic data set for further manual analysis.
Optionally, the method further includes: analyzing the refined analysis result of the dealer terminal according to a preset rule, and determining the adjustment type of the dealer so as to adjust the sales mode data of the dealer terminal according to the adjustment type;
wherein the adjustment types include: a support type, a restriction type, and a preference type. The device herein may be a server, a PC, a PAD, a mobile phone, etc.
The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device:
determining a market situation dynamic data set of a plurality of dealer terminals in the current period;
acquiring a result change data set of a plurality of dealer terminals in the current period;
for each dealer terminal, performing cross combination on the market condition dynamic data set and the result change data set to obtain a relation data set;
and comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals to obtain a refined analysis result.
Optionally, the determining the market condition dynamic data set of the plurality of dealer terminals in the current period includes:
for each dealer terminal: acquiring an external market dynamic data set and an internal market dynamic data set of a dealer terminal in a current period, and forming the market dynamic data set of the current period by the external market dynamic data set and the internal market dynamic data; wherein the content of the first and second substances,
the external market dynamics data set includes: whether the current time is at least one of a judgment result of the busy season, a judgment result of whether a competitor has activities and a judgment result of policy movement;
the internal market dynamics data set includes: at least one of a judgment result of whether there is a discount, a judgment result of whether there is a promotion, and a judgment result of whether there is a new product on the market.
Optionally, the acquiring a result change data set of a plurality of dealer terminals in the current period includes:
acquiring current performance data of a plurality of dealer terminals in a current period;
calculating according to a preset rule to obtain a current result data set based on the current performance data and the historical performance data;
comparing the current result data set with the historical result data set to obtain a result change data set;
wherein the result change data set comprises result change data corresponding to a plurality of types of performance data;
if the performance data corresponding to one type in the current performance data set and the performance data corresponding to the same type in the historical performance data are within a preset range, the result change data corresponding to the type is a stable identifier;
if the performance data corresponding to one type in the current performance data set is larger than the performance data corresponding to the same type in the historical performance data, the result change data corresponding to the type is an ascending identifier;
and if the performance data corresponding to one type in the current performance data set is smaller than the performance data corresponding to the same type in the historical performance data, the result change data corresponding to the type is a descending identifier.
Optionally, the cross-combining the market condition dynamic data set and the result variation data set to obtain a relationship data set includes:
for each quotation dynamic data in the quotation dynamic data set, respectively constructing relationship data with each type of result change data in the result change data set;
a plurality of relational data make up the relational data set.
Optionally, the comprehensively analyzing the relationship data sets corresponding to the multiple dealer terminals to obtain a refined analysis result includes:
comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals, and analyzing the influence degree of the obtained different market situation dynamic data on each result change data corresponding to each dealer terminal;
and analyzing comprehensively the corresponding relation data sets of the plurality of dealer terminals to obtain one or more market situation dynamic data which cause different result change data corresponding to each dealer terminal.
Optionally, the dealer terminal is represented by an essential feature data set; wherein the essential feature data set comprises:
the method comprises the following steps of determining the type of an industry to which a dealer belongs, the location district to which the dealer belongs, the administrative level to which the dealer belongs, the judgment result of whether related products are sold in parallel, the type of an store to which the dealer belongs, the personnel service quality level, the service personnel efficiency level, the hardware equipment level of the store and an after-sale management mode;
the data analysis method comprises:
and screening and displaying the refined analysis results of the target dealer terminal from the plurality of dealer terminals according to one or more fields in the essential characteristic data set for further manual analysis.
Optionally, the method further includes: analyzing the refined analysis result of the dealer terminal according to a preset rule, and determining the adjustment type of the dealer so as to adjust the sales mode data of the dealer terminal according to the adjustment type;
wherein the adjustment types include: a support type, a restriction type, and a preference type.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A data analysis method is applied to an enterprise server, wherein the enterprise server is connected with a plurality of dealer terminals; the data analysis method comprises the following steps:
determining a market situation dynamic data set of a plurality of dealer terminals in the current period;
acquiring a result change data set of a plurality of dealer terminals in the current period;
for each dealer terminal, performing cross combination on the market condition dynamic data set and the result change data set to obtain a relation data set;
and comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals to obtain a refined analysis result.
2. The method of claim 1, wherein determining the market activity data set for the plurality of dealer terminals for the current period comprises:
for each dealer terminal: acquiring an external market dynamic data set and an internal market dynamic data set of a dealer terminal in a current period, and forming the market dynamic data set of the current period by the external market dynamic data set and the internal market dynamic data; wherein the content of the first and second substances,
the external market dynamics data set includes: whether the current time is at least one of a judgment result of the busy season, a judgment result of whether a competitor has activities and a judgment result of policy movement;
the internal market dynamics data set includes: at least one of a judgment result of whether there is a discount, a judgment result of whether there is a promotion, and a judgment result of whether there is a new product on the market.
3. The method of claim 1 or 2, wherein obtaining the change data set resulting from the plurality of dealer terminals in the current cycle comprises:
acquiring current performance data of a plurality of dealer terminals in a current period;
calculating according to a preset rule to obtain a current result data set based on the current performance data and the historical performance data;
comparing the current result data set with the historical result data set to obtain a result change data set;
wherein the result change data set comprises result change data corresponding to a plurality of types of performance data;
if the performance data corresponding to one type in the current performance data set and the performance data corresponding to the same type in the historical performance data are within a preset range, the result change data corresponding to the type is a stable identifier;
if the performance data corresponding to one type in the current performance data set is larger than the performance data corresponding to the same type in the historical performance data, the result change data corresponding to the type is an ascending identifier;
and if the performance data corresponding to one type in the current performance data set is smaller than the performance data corresponding to the same type in the historical performance data, the result change data corresponding to the type is a descending identifier.
4. The method of claim 3, wherein the cross-combining the market dynamics data set and the results variation data set to obtain a relational data set comprises:
for each quotation dynamic data in the quotation dynamic data set, respectively constructing relationship data with each type of result change data in the result change data set;
a plurality of relational data make up the relational data set.
5. The method of claim 3, wherein the comprehensively analyzing the relational data sets corresponding to the plurality of dealer terminals to obtain refined analysis results comprises:
comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals, and analyzing the influence degree of the obtained different market situation dynamic data on each result change data corresponding to each dealer terminal;
and analyzing comprehensively the corresponding relation data sets of the plurality of dealer terminals to obtain one or more market situation dynamic data which cause different result change data corresponding to each dealer terminal.
6. The method of claim 5, wherein the distributor terminal is represented using an intrinsic feature dataset; wherein the essential feature data set comprises:
the method comprises the following steps of determining the type of an industry to which a dealer belongs, the location district to which the dealer belongs, the administrative level to which the dealer belongs, the judgment result of whether related products are sold in parallel, the type of an store to which the dealer belongs, the personnel service quality level, the service personnel efficiency level, the hardware equipment level of the store and an after-sale management mode;
the data analysis method further comprises:
and screening and displaying the refined analysis results of the target dealer terminal from the plurality of dealer terminals according to one or more fields in the essential characteristic data set for further manual analysis.
7. The method of any one of claims 1-6, further comprising:
analyzing the refined analysis result of the dealer terminal according to a preset rule, and determining the adjustment type of the dealer so as to adjust the sales mode data of the dealer terminal according to the adjustment type;
wherein the adjustment types include: a support type, a restriction type, and a preference type.
8. A data analysis device is applied to an enterprise server, wherein the enterprise server is connected with a plurality of dealer terminals; the data analysis device includes:
the system comprises a determining unit, a processing unit and a processing unit, wherein the determining unit is used for determining market situation dynamic data sets of a plurality of dealer terminals in the current period;
the acquiring unit is used for acquiring a result change data set of a plurality of dealer terminals in the current period;
the combination unit is used for carrying out cross combination on the market situation dynamic data set and the result change data set to obtain a relation data set for each dealer terminal;
and the analysis unit is used for comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals to obtain a refined analysis result.
9. A data analysis system, comprising:
an enterprise server, and a plurality of dealer terminals connected to the enterprise server;
the enterprise server is used for determining the market situation dynamic data set of the plurality of dealer terminals in the current period;
acquiring a result change data set of a plurality of dealer terminals in the current period; for each dealer terminal, performing cross combination on the market condition dynamic data set and the result change data set to obtain a relation data set; and comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals to obtain a refined analysis result.
10. An apparatus comprising a processor, a memory, and a program stored on the memory and executable on the processor, the processor implementing the steps when executing the program of:
determining a market situation dynamic data set of a plurality of dealer terminals in the current period;
acquiring a result change data set of a plurality of dealer terminals in the current period;
for each dealer terminal, performing cross combination on the market condition dynamic data set and the result change data set to obtain a relation data set;
and comprehensively analyzing the relationship data sets corresponding to the plurality of dealer terminals to obtain a refined analysis result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811144715.9A CN110968611A (en) | 2018-09-29 | 2018-09-29 | Data analysis method, device and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811144715.9A CN110968611A (en) | 2018-09-29 | 2018-09-29 | Data analysis method, device and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110968611A true CN110968611A (en) | 2020-04-07 |
Family
ID=70027692
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811144715.9A Pending CN110968611A (en) | 2018-09-29 | 2018-09-29 | Data analysis method, device and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110968611A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113781005A (en) * | 2021-09-22 | 2021-12-10 | 南方电网数字电网研究院有限公司 | Power grid human resource allocation and allocation system |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6587841B1 (en) * | 1995-09-12 | 2003-07-01 | First American Credit Management Solutions, Inc. | Computer implemented automated credit application analysis and decision routing system |
| CN1464448A (en) * | 2002-06-25 | 2003-12-31 | 鸿富锦精密工业（深圳）有限公司 | Mobile sales accessory system and method thereof |
| US7181427B1 (en) * | 1995-09-12 | 2007-02-20 | Jp Morgan Chase Bank, N.A. | Automated credit application system |
| CN105264556A (en) * | 2013-03-12 | 2016-01-20 | 斯特拉斯佩皇冠有限公司 | Systems and methods for market analysis and automated business decisioning |
| CN106203840A (en) * | 2016-07-13 | 2016-12-07 | 欧阳张睿 | Scheme information automatic generation method |
| CN106651413A (en) * | 2015-10-30 | 2017-05-10 | 顶益（开曼岛）控股有限公司 | Container paste, mobile terminal, data analysis system and network sales platform |
| CN106920124A (en) * | 2017-02-20 | 2017-07-04 | 湖南云连天地网络科技有限公司 | A kind of Data acquisition and issuance method and device |
| CN107025548A (en) * | 2016-09-21 | 2017-08-08 | 阿里巴巴集团控股有限公司 | Transaction processing method, terminal and system |
| CN107784510A (en) * | 2016-08-24 | 2018-03-09 | 上海零氏信息技术有限公司 | Sales achievement statistical analysis system and method based on shops's retail terminal |
| CN107886391A (en) * | 2017-10-23 | 2018-04-06 | 兰世德 | A kind of cross-border vertical electric business management method |
| CN107977858A (en) * | 2017-11-14 | 2018-05-01 | 深圳市思迅软件股份有限公司 | Sales data statistical method, device, server and readable storage medium storing program for executing |
| CN108021993A (en) * | 2017-12-25 | 2018-05-11 | 郑州跃龙电子科技有限公司 | A kind of water purifier dealer management end based on Internet of Things |
| CN108038216A (en) * | 2017-12-22 | 2018-05-15 | 联想(北京)有限公司 | Information processing method, device and server cluster |
| CN108335149A (en) * | 2018-04-26 | 2018-07-27 | 昆山德恩基电子科技有限公司 | It is a kind of to collect system and method for the processing commodity to consumer appeal |
| CN108363800A (en) * | 2018-02-24 | 2018-08-03 | 孙如妍 | Business intelligence is analyzed and big data system |
| CN108428146A (en) * | 2017-12-21 | 2018-08-21 | 中国平安人寿保险股份有限公司 | Promoting service method, apparatus and storage medium |
-
2018
- 2018-09-29 CN CN201811144715.9A patent/CN110968611A/en active Pending
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6587841B1 (en) * | 1995-09-12 | 2003-07-01 | First American Credit Management Solutions, Inc. | Computer implemented automated credit application analysis and decision routing system |
| US7181427B1 (en) * | 1995-09-12 | 2007-02-20 | Jp Morgan Chase Bank, N.A. | Automated credit application system |
| US20090234769A9 (en) * | 1995-09-12 | 2009-09-17 | Defrancesco James | Computer implemented automated credit application analysis and decision routing system |
| CN1464448A (en) * | 2002-06-25 | 2003-12-31 | 鸿富锦精密工业（深圳）有限公司 | Mobile sales accessory system and method thereof |
| CN105264556A (en) * | 2013-03-12 | 2016-01-20 | 斯特拉斯佩皇冠有限公司 | Systems and methods for market analysis and automated business decisioning |
| CN106651413A (en) * | 2015-10-30 | 2017-05-10 | 顶益（开曼岛）控股有限公司 | Container paste, mobile terminal, data analysis system and network sales platform |
| CN106203840A (en) * | 2016-07-13 | 2016-12-07 | 欧阳张睿 | Scheme information automatic generation method |
| CN107784510A (en) * | 2016-08-24 | 2018-03-09 | 上海零氏信息技术有限公司 | Sales achievement statistical analysis system and method based on shops's retail terminal |
| CN107025548A (en) * | 2016-09-21 | 2017-08-08 | 阿里巴巴集团控股有限公司 | Transaction processing method, terminal and system |
| CN106920124A (en) * | 2017-02-20 | 2017-07-04 | 湖南云连天地网络科技有限公司 | A kind of Data acquisition and issuance method and device |
| CN107886391A (en) * | 2017-10-23 | 2018-04-06 | 兰世德 | A kind of cross-border vertical electric business management method |
| CN107977858A (en) * | 2017-11-14 | 2018-05-01 | 深圳市思迅软件股份有限公司 | Sales data statistical method, device, server and readable storage medium storing program for executing |
| CN108428146A (en) * | 2017-12-21 | 2018-08-21 | 中国平安人寿保险股份有限公司 | Promoting service method, apparatus and storage medium |
| CN108038216A (en) * | 2017-12-22 | 2018-05-15 | 联想(北京)有限公司 | Information processing method, device and server cluster |
| CN108021993A (en) * | 2017-12-25 | 2018-05-11 | 郑州跃龙电子科技有限公司 | A kind of water purifier dealer management end based on Internet of Things |
| CN108363800A (en) * | 2018-02-24 | 2018-08-03 | 孙如妍 | Business intelligence is analyzed and big data system |
| CN108335149A (en) * | 2018-04-26 | 2018-07-27 | 昆山德恩基电子科技有限公司 | It is a kind of to collect system and method for the processing commodity to consumer appeal |
Non-Patent Citations (1)
| Title |
|---|
| 中国石油规划总院市场营销研究所: "中国沥青、燃料油及润滑油市场月度分析", 《国际石油经济》, pages 72 - 75 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113781005A (en) * | 2021-09-22 | 2021-12-10 | 南方电网数字电网研究院有限公司 | Power grid human resource allocation and allocation system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20180247263A1 (en) | Method and apparatus for adjusting merchant delivery range | |
| US8631040B2 (en) | Computer-implemented systems and methods for flexible definition of time intervals | |
| US8255264B2 (en) | Methods and systems for determining the effectiveness of a dealer's ability to close a sale | |
| US20140067472A1 (en) | System and Method For Segmenting A Customer Base | |
| CN108629436B (en) | Method and electronic equipment for estimating warehouse goods picking capacity | |
| CN109583729B (en) | Data processing method and device for platform online model | |
| CA3108904A1 (en) | System and method for retail price optimization | |
| CN110766232B (en) | Dynamic prediction method and system thereof | |
| CN108171530B (en) | Method and device for improving unit price and repurchase rate of customers | |
| CN110019774B (en) | Label distribution method, device, storage medium and electronic device | |
| CN115115417A (en) | Public opinion-based commodity sales data prediction method, equipment and medium | |
| CN110968611A (en) | Data analysis method, device and system | |
| CN114708007A (en) | Intelligent decomposition method and system for store sales plan | |
| CN113553234A (en) | Data anomaly detection method | |
| US20190164180A1 (en) | Methods, systems, apparatus and articles of manufacture to generate projection weights for a panel | |
| US7908165B2 (en) | Method and apparatus for generating performance curves | |
| CN116680494A (en) | Method and device for generating application recommendation page, storage medium and electronic equipment | |
| CN109614548B (en) | Information display method and device based on service user number change rate and storage medium | |
| CN110968487A (en) | Abnormal data analysis method and device | |
| CN116596547A (en) | Client relationship management method and system based on multidimensional information data | |
| CN113051526A (en) | Quantization evaluation method and device | |
| US20140222515A1 (en) | Systems and methods for enhanced principal components analysis | |
| CN109697203A (en) | Index unusual fluctuation analysis method and equipment, computer storage medium, computer equipment | |
| CN114048252A (en) | Method and device for analyzing production data, storage medium and processor | |
| CN114444934A (en) | Enterprise sales periodic evaluation algorithm and tool application thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200407 |